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Aaron Petkau is a bioinformatician working for the Public Health Agency of Canada’s National Microbiology Laboratory and the current Head of Bioinformatics Pipeline Development within the Bioinformatics unit of the laboratory. His work primarily focuses on the development of bioinformatics software for infectious disease genomics. Some of his projects have included: developing tools for comparative genomics (GView and GView Server), phylogenetic analysis of microbial genomes (SNVPhyl), the management of genomics data (IRIDA), and indexing, querying, and visualization of mutations or genes derived from collections of microbial genomes (Genomics Data Index). He is currently focused on the development and integration of a diverse set of bioinformatics pipelines into a larger system for routine use within the Public Health Agency of Canada.
Dr. Alan Moses is a Professor in the Department of Cell and Systems Biology and Computer Science. He completed his Bachelor’s degree at Columbia University and his Ph.D. at the University of California, Berkeley. The Moses Lab seeks to understand how are regulatory networks are encoded in genome sequences. One of the important components of this ‘regulatory code’ has been discovered: ‘regulatory motifs’ in the sequences of DNA, RNA and proteins. One of the major aims of their research is to develop computational and statistical tools to identify these motifs. Just as differential gene activity can explain cellular and physiological diversity within a single organism (nerve cell vs. white blood cell with same genes), it has also been proposed as explanation for physiological and morphological differences between closely related organisms (chimpanzee vs. human with very similar genes). They seek to understand how regulatory networks are sculpted by evolution. They focus on the evolution of regulatory motifs because, by mediating the regulatory interactions, they specify the connections in regulatory networks. Their goal is to translate the evolutionary differences in regulatory motifs to quantitative differences in regulatory networks, and ultimately, to their impact on organismal fitness. In 2015, Dr. Moses was awarded as Canada Research Chair in Computational Biology, which was renewed in 2020.
Ali Bashashati’s research area lies at the interface between computational, engineering and biomedical sciences. He is interested in developing machine learning, signal processing algorithms and software infrastructure to combine various sources of omics and imaging data. He has published extensively in cancer genomics, computational pathology, bioinformatics and computational biology and his papers have appeared in top-tier journals such as Nature, Nature Genetics, Nature Communications, and Nature Medicine.
Dr. Aline Talhouk is an assistant professor in the Department of Obstetrics and Gynecology in the Faculty of Medicine at the University of British Columbia (UBC). Dr. Talhouk holds a Ph.D. from UBC and has expertise in computational statistics and machine learning, specifically focusing on developing and implementing predictive models in women’s health and oncology. Her research leverages statistical computing, machine learning and artificial intelligence to translate -omics discoveries into clinical applications and bring individualized care to ovarian and endometrial cancer patients. Dr. Talhouk has also developed a nationally-funded precision prevention program that uses prediction modelling to identify those at high risk for uterine cancer and direct them to risk-reducing interventions, targeted screening and surveillance. Dr. Talhouk is a Michael Smith Health Research BC Scholar and holds several grants from the Canadian Institutes of Health Research, the Canada Foundation for Innovation and the Canadian Cancer Society.
Dr. Singh is an Assistant Professor of Heart and Lung Pharmacogenomics in the Department of Anesthesiology, Pharmacology and Therapeutics at UBC and a Principal Investigator at the Centre for Heart Lung Innovation. Dr. Singh leads a computational biology lab focused on biomarker discovery and developing methods and tools for multiomics data integration and visualization. He is a reviewer for academic journals such as Nature Methods, Cells, PLOS Comp Bio, Bioinformatics, and JACC. He also sits on various committees such as his department’s EDI committee, and the Bruce McManus Cardiovascular Biobank committee. Dr. Singh is passionate about educating researchers about omics-based methodologies, tools for biomarker discovery, reproducible data analysis, version control and interactive visualizations (making content available on his YouTube channel or open source workshops materials on GitHub, see lab website for details).
Dr. Lee joined the Department of Molecular Biology and Biochemistry at Simon Fraser University as an Assistant Professor in 2020. She completed her PhD at the Department of Cell and Systems Biology at University of Toronto with Drs. David Guttman and Darrell Desveaux, studying the evolutionary arms race during host-pathogen interaction. She then moved to University of British Columbia (UBC) to a 2-year postdoctoral fellowship with Dr. Corey Nislow, where she used comparative bacterial genomics and phenomics to understand how bacterial pathogens adapt to cause persistent long-term infections. This was followed by a productive 3-year postdoctoral fellowship with Dr. Bob Hancock at UBC applying systems immunology and vaccinology to understand neonatal immune development. Her currently research uses systems biology approaches to improve neonatal sepsis diagnostics and combat antimicrobial resistance.
Dr. Andrew Doxey is a bioinformatician and Associate Professor in the Department of Biology at the University of Waterloo, where he holds a University Research Chair. He is cross-appointed to the Cheriton School of Computer Science, and is also an adjunct Professor in the Department of Medicine at McMaster University. The Doxey lab focuses on bioinformatics, microbial genomics, and molecular evolution, and applies computational methods to discover new protein families and functions. Recent work includes the development of the AnnoTree phylogenomics database, and studies on the evolution of bacterial toxins. Dr. Doxey is supported by grants from NSERC and MITACS, and is the recipient of the 2018 Thermo Fisher Award for contributions to microbiology.
Dr. McArthur is a Professor and David Braley Chair in Computational Biology at McMaster University and has had a career in the United States and Canada, including NIH-funded positions at the Marine Biological Laboratory and Brown University, where he led the genome assembly of the diarrheal pathogen Giardia intestinalis, plus 10 years of experience in the private sector. Dr. McArthur’s research team focuses on building tools, databases, and algorithms for the genomic surveillance of infectious pathogens. He and his team developed the Comprehensive Antibiotic Resistance Database (card.mcmaster.ca) and the SARS-CoV-2 Illumina GeNome Assembly Line software platform.
Dr Goldenberg is a Senior Scientist in Genetics and Genome Biology program at SickKids Research Institute, recently appointed as the first Varma Family Chair in Biomedical Informatics and Artificial Intelligence. She is also an Associate Professor in the Department of Computer Science at the University of Toronto, faculty member and an Associate Research Director, Health at Vector Institute and a fellow at the Canadian Institute for Advanced Research (CIFAR), Child and Brain Development group. Dr Goldenberg trained in machine learning at Carnegie Mellon University, with a post-doctoral focus in computational biology and medicine. The current focus of her lab is on developing machine learning methods that capture heterogeneity and identify disease mechanisms in complex human diseases as well as developing risk prediction and early warning clinical systems. Dr Goldenberg is a recipient of the Early Researcher Award from the Ministry of Research and Innovation and a Canada Research Chair in Computational Medicine. She is strongly committed to creating responsible AI to benefit patients across a variety of conditions.
Anna Panchenko completed her PhD at Lomonosov Moscow State University in Russia and pursued post-doctoral research at the University of Illinois at Urbana-Champaign and the National Center for Biotechnology Information (NCBI) at the National Institutes of Health in the USA. As a Lead Scientist at NCBI, her focus was on creating computational methods for the analysis of protein interactions, cancer driver events, and chromatin dynamics. In 2019, she joined Queen’s University as a professor and as a Canada Research Chair Tier 1, alongside a Senior Investigator role at the Ontario Institute of Cancer Research in Toronto. Her research lab delves into the links between essential epigenomic elements to uncover how their disturbances may cause cancer. Anna has authored a book on protein interactions and over 100 peer-reviewed articles. She is an editor for several scientific journals, including the Journal of Molecular Biology, eLife, and Chromosoma.
Anthony Kusalik received his M.Sc. and Ph.D. from the University of British Columbia in Vancouver, B.C., Canada in 1982 and 1988, respectively. Dr. Kusalik began a faculty position at the University of Saskatchewan in 1985. He is now a professor emeritus in the Department of Computer Science at the University of Saskatchewan. Dr. Kusalik has served on NIH/NIAAD, NSERC, and CIHR grant review panels, and on program committees of numerous bioinformatics conferences. He is a member of the ISCB.
Arnaud Droit is a full professor in bioinformatics in the Faculty of Medicine of Laval University. He is the director of the bioinformatics and proteomics platforms of the Research Center of the CHU de Québec – Université Laval. His laboratory focuses on the development of tools dedicated to the analysis of omics-type massive data, including genomics, transcriptomics, proteomics and metabolomics. His work provides a better understanding of the complex biological mechanisms of different diseases or biological phenomena. His team develops various approaches to identify multi-omics signatures using multivariate-driven methods such as machine learning and knowledge-based methods such as interaction networks.